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neuPrint#

Query and fetch neurons and connectivity from a neuPrint server.

NeuPrint is a service for presenting and analyzing connectomics data. It is used to host, for example, the Janelia EM reconstructions from a Drosophila hemibrain at https://neuprint.janelia.org/.

neuprint-python is a Python library that lets you query data directly from a neuPrint server. You can install it from PyPI:

pip3 install neuprint-python

navis.interfaces.neuprint wraps neuprint-python and adds a few new functions to fetch and convert data into NAVis objects.

Requires network access and a token

This tutorial talks to a live neuPrint server, so it needs an internet connection. You will also need a neuPrint account and an API token - see the "Authentication" note below for how to set it.

Import navis

import navis

# Import neuprint wrapper by navis
import navis.interfaces.neuprint as neu

Set up the connection#

Authentication

Pass your API token directly via token=..., or store it as a NEUPRINT_APPLICATION_CREDENTIALS environment variable. The latter is the recommended approach and the one we use here.

client = neu.Client(
    "https://neuprint.janelia.org/",
    # token="MYLONGTOKEN"  # use this to instead pass your token directly
    dataset="hemibrain:v1.2.1",
)

You can use all of neuprint's functions:

mbons, roi_info = neu.fetch_neurons(
    neu.SegmentCriteria(instance=".*MBON.*", regex=True)
)
mbons.head(3)
bodyId instance type pre post downstream upstream mito size status cropped statusLabel cellBodyFiber somaRadius somaLocation roiInfo notes inputRois outputRois
0 300972942 MBON14(a3)_R MBON14 543 13634 4336 13634 283 1566415099 Traced False Roughly traced None NaN None {'MB(R)': {'pre': 17, 'post': 13295, 'downstre... None [MB(+ACA)(R), MB(R), SIP(R), SLP(R), SMP(R), S... [MB(+ACA)(R), MB(R), SIP(R), SLP(R), SMP(R), S...
1 394225044 MBON14(a3)_L MBON14 187 5172 768 5172 142 359288547 Traced False Roughly traced None NaN None {'MB(L)': {'pre': 185, 'post': 5143, 'downstre... None [MB(L), SIP(L), SNP(L), aL(L)] [MB(L), SIP(L), SNP(L), aL(L)]
2 422725634 MBON06(B1>a)(AVM07)_L MBON06 1356 21000 9579 21000 765 3157840413 Traced False Roughly traced None NaN None {'MB(R)': {'pre': 777, 'post': 20390, 'downstr... None [CRE(-ROB,-RUB)(R), CRE(R), INP, MB(+ACA)(R), ... [CRE(-ROB,-RUB)(R), CRE(R), INP, MB(+ACA)(R), ...

On top of neuprint-python's own functions, NAVis adds three that return NAVis objects:

Function Returns
fetch_roi a navis.Volume from a ROI
fetch_skeletons fully-fledged navis.Skeletons - nodes, synapses, soma and all
fetch_mesh_neuron navis.Meshes - including synapses

Start by fetching the mesh for the right mushroom body ROI:

mb = neu.fetch_roi("MB(R)")
mb

Out:

<navis.Volume(name=MB(R), units=1 dimensionless, color=(0.85, 0.85, 0.85, 0.2), vertices.shape=(57913, 3), faces.shape=(115856, 3))>

Next, let's fetch the skeletons of all right MBONs:

mbon_skeletons = neu.fetch_skeletons(
    neu.SegmentCriteria(instance=".*MBON.*_R", regex=True), with_synapses=True
)
mbon_skeletons.head()

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type name id n_nodes n_connectors n_branches n_leafs cable_length soma units
0 navis.Skeleton MBON14(a3)_R 300972942 22843 14177 2298 2370 7.585365e+05 NaN 8 nanometer
1 navis.Skeleton MBON23(a2sp)_R 423382015 14570 5241 1224 1253 4.588212e+05 2379.0 8 nanometer
2 navis.Skeleton MBON19(a2p3p)_R 423774471 6944 1783 549 561 2.464995e+05 1781.0 8 nanometer
3 navis.Skeleton MBON11(y1pedc>a/B)_R 424767514 40011 29288 4169 4290 1.592890e+06 5491.0 8 nanometer
4 navis.Skeleton MBON02(B2B'2a)_R 424789697 34243 20709 3040 3095 1.435912e+06 34135.0 8 nanometer

Co-visualize the MBONs and the MB volume:

navis.plot3d(
    [mbon_skeletons[0], mb],
    legend=False,  # Hide the legend (more space for the plot)
)

Last (but not least), let's make a 2d plot for the tutorial's thumbnail:

import matplotlib.pyplot as plt

fig, ax = navis.plot2d(
    [mbon_skeletons[0], mb],
    c=(0, 0, 0, 1),  # Make the neuron black
    method="3d",
    connectors=True,
    linewidth=0.5,  # Make neuron a bit thinner to emphasize the synapses
    view=("x", "-z"),
)

plt.tight_layout()

tutorial remote 00 neuprint

All NAVis functions for analysis & visualization should work on these neurons. If not, please open an issue on Github.

Total running time of the script: ( 0 minutes 13.794 seconds)

Download Python source code: tutorial_remote_00_neuprint.py

Download Jupyter notebook: tutorial_remote_00_neuprint.ipynb

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